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1. Conditional compilation is dead, long live conditional compilation!

   https://doi.org/10.1109/ICSE-NIER.2019.00035  

   Gazzillo, Paul; Wei, Shiyi (May 2019, Proceedings - International Conference on Software Engineering)

   Highly-configurable systems written in C form our most critical computing infrastructure. The preprocessor is integral to C, because conditional compilation enables such systems to produce efficient object code. However, the preprocessor makes code harder to reason about for both humans and tools. Previous approaches to this challenge developed new program analyses for unpreprocessed source code or developed new languages and constructs to replace the preprocessor. But having specialpurpose analyses means maintaining a new toolchain, while new languages face adoption challenges and do not help with existing software. We propose the best of worlds: eliminate the preprocessor but preserve its benefits. Our design replaces preprocessor usage with C itself, augmented with syntax-preserving, backwards-compatible dependent types. We discuss automated conditional compilation to replicate preprocessor performance. Our approach opens new directions for research into new compiler optimizations, dependent types for configurable software, and automated translation away from preprocessor use. 

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2. Blended Conditional Gradients

   Braun, G; Pokutta, S; Tu, D; Wright, S (January 2019, Proceedings of Machine Learning Research)
3. Conditional Linear Regression

   Calderon, Diego; Juba, Brendan; Li, Sirui; Li, Zongyi; Ruan, Lisa (August 2020, Proceedings of Machine Learning Research)

   Work in machine learning and statistics commonly focuses on building models that capture the vast majority of data, possibly ignoring a segment of the population as outliers. However, there may not exist a good, simple model for the distribution, so we seek to find a small subset where there exists such a model. We give a computationally efficient algorithm with theoretical analysis for the conditional linear regression task, which is the joint task of identifying a significant portion of the data distribution, described by a k-DNF, along with a linear predictor on that portion with a small loss. In contrast to work in robust statistics on small subsets, our loss bounds do not feature a dependence on the density of the portion we fit, and compared to previous work on conditional linear regression, our algorithm’s running time scales polynomially with the sparsity of the linear predictor. We also demonstrate empirically that our algorithm can leverage this advantage to obtain a k-DNF with a better linear predictor in practice. 

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4. Conditional Davis pricing

   https://doi.org/10.1007/s00780-020-00424-5  

   Larsen, Kasper; Soner, Halil Mete; Žitković, Gordan (July 2020, Finance and Stochastics)
5. Federated Conditional Stochastic Optimization

   Wu, Xidong; Sun, Jianhui; Hu, Zhengmian; Li, Junyi; Zhang, Aidong; Huang, Heng (December 2023, Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023))